Launch a production-ready AI product in 8-12 weeks. We build minimum viable products that work properly from day one, then iterate based on real usage.
An MVP is not a demo or a prototype. It is a real product that real users can rely on, stripped to essentials so you can launch quickly and learn from actual usage. We build AI products that work properly from day one.
Minimum viable product does not mean minimum quality. It means shipping the smallest thing that delivers genuine value, so you can start learning immediately instead of building in isolation.
For AI applications, this requires judgement. Too little capability and users will not engage. Too much and you waste months building features nobody needs. We help you find the right scope.
We follow a structured process designed for speed without sacrificing quality.
Scope definition identifies the core use case. What is the one thing this product must do well? What can wait for later versions? We make these decisions together, balancing ambition against delivery constraints.
Architecture planning designs for the future while building for today. MVP code should be replaceable, but the foundations should be solid. We create systems that can evolve as you learn.
Iterative delivery produces working software in short cycles. You see progress weekly, can give feedback early, and can adjust direction as understanding develops.
Production deployment puts the MVP in front of real users with proper infrastructure. This is not a staging environment. It is live, monitored, and supported.
A typical AI MVP includes:
Core functionality for the primary use case. The “must work” workflow with the quality bar defined.
A user interface that fits the audience. Simple, clear interactions that make the system usable day one.
Essential integrations. Secure connections to the systems the MVP needs to be genuinely useful.
Monitoring and logging. Visibility into performance, errors, and model behaviour so you can improve safely.
Documentation. Enough guidance for users and administrators to operate the MVP confidently.
A launch support plan. Clear ownership for issues and a sensible pathway for iteration.
We also deliver the learning infrastructure: analytics, feedback collection, and reporting that help you understand how users actually behave.
Most AI MVPs launch in eight to twelve weeks. Simpler products can move faster. Complex applications with extensive integration may take longer.
MVP is the beginning, not the end. Once live, you will discover what users really need, which features matter, and where the product falls short. This learning is the point.
We can continue working with you after launch, iterating based on evidence.
MVP development suits organisations that:
Want to test a product concept with real users. You need a real product to learn from, not a lab project.
Need to move quickly. Speed matters, but you still need a reliable foundation and clear ownership.
Prefer learning from usage over extensive upfront planning. You want to make decisions based on real behaviour.
Have resources for launch and iteration. An MVP is most valuable when you can act on learning.
If you need to validate an idea before building anything, consider a [proof of concept](/services/proof-of-concept/) first. If you have a proven concept ready for serious development, MVP is the logical next step.
Use these prompts to sharpen scope and define what “viable” means for your users.
“What is the single core workflow our MVP must do well, and what should we explicitly defer?”
“What success metrics should we track from day one: adoption, accuracy, time saved, and failure modes?”
“What risks will stop this working in production (data, integration, governance), and how do we mitigate them?”
We prioritise ruthlessly based on user value and feasibility. Features that are nice-to-have wait for later versions. Features that are essential go in.
We choose technology based on requirements, not preference.
We expect requirements to evolve. Short delivery cycles mean you can adjust direction frequently without major disruption.
You do. We build products for our clients to own and control. You receive full source code and documentation.
We define the smallest valuable workflow, ship quickly, and use analytics and user feedback to decide what to build next.
AI adds uncertainty. You need evaluation, monitoring, fallbacks, and clear human oversight where consequences matter.
Sometimes. If feasibility is unclear or data quality is unknown, a PoC can validate assumptions before an MVP build.
We set a quality bar, test against realistic scenarios, monitor behaviour in production, and iterate based on evidence.
We review usage, performance, and failure modes, then prioritise the next iteration.